# A Holistic Indicator of Polarization to Measure Online Sexism

arXiv: https://arxiv.org/abs/2404.02205 · HTML: https://mlsanity.com/paper/2404.02205/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 17 of its 43 references that are in the map.
- First posted on arXiv 2 Apr 2024.

## Builds on (most cited first, 17 of 17)

- [WILDS: A Benchmark of in-the-Wild Distribution Shifts](https://mlsanity.com/paper/2012.07421/) (2020)
- [Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings](https://mlsanity.com/paper/1607.06520/) (2016)
- [Semantics derived automatically from language corpora contain human-like biases](https://mlsanity.com/paper/1608.07187/) (2016)
- [The Pushshift Reddit Dataset](https://mlsanity.com/paper/2001.08435/) (2020)
- [Ex Machina: Personal Attacks Seen at Scale](https://mlsanity.com/paper/1610.08914/) (2016)
- [SemEval-2019 Task 6: Identifying and Categorizing Offensive Language in Social Media (OffensEval)](https://mlsanity.com/paper/1903.08983/) (2019)
- [Understanding Undesirable Word Embedding Associations](https://mlsanity.com/paper/1908.06361/) (2019)
- [You Only Prompt Once: On the Capabilities of Prompt Learning on Large Language Models to Tackle Toxic Content](https://mlsanity.com/paper/2308.05596/) (2023)
- [What are the biases in my word embedding?](https://mlsanity.com/paper/1812.08769/) (2018)
- [Shirtless and Dangerous: Quantifying Linguistic Signals of Gender Bias in an Online Fiction Writing Community](https://mlsanity.com/paper/1603.08832/) (2016)
- [Kek, Cucks, and God Emperor Trump: A Measurement Study of 4chan's Politically Incorrect Forum and Its Effects on the Web](https://mlsanity.com/paper/1610.03452/) (2016)
- [Discovering and Categorising Language Biases in Reddit](https://mlsanity.com/paper/2008.02754/) (2020)
- [AI in the Gray: Exploring Moderation Policies in Dialogic Large Language Models vs. Human Answers in Controversial Topics](https://mlsanity.com/paper/2308.14608/) (2023)
- [Automatic Sexism Detection with Multilingual Transformer Models](https://mlsanity.com/paper/2106.04908/) (2021)
- [Addressing machine learning concept drift reveals declining vaccine sentiment during the COVID-19 pandemic](https://mlsanity.com/paper/2012.02197/) (2020)
- ["How over is it?" Understanding the Incel Community on YouTube](https://mlsanity.com/paper/2001.08293/) (2020)
- [The Evolution of the Manosphere Across the Web](https://mlsanity.com/paper/2001.07600/) (2020)

## Abstract (arXiv)

The online trend of the manosphere and feminist discourse on social networks requires a holistic measure of the level of sexism in an online community. This indicator is important for policymakers and moderators of online communities (e.g., subreddits) and computational social scientists, either to revise moderation strategies based on the degree of sexism or to match and compare the temporal sexism across different platforms and communities with real-time events and infer social scientific insights. In this paper, we build a model that can provide a comparable holistic indicator of toxicity targeted toward male and female identity and male and female individuals. Despite previous supervised NLP methods that require annotation of toxic comments at the target level (e.g. annotating comments that are specifically toxic toward women) to detect targeted toxic comments, our indicator uses supervised NLP to detect the presence of toxicity and unsupervised word embedding association test to detect the target automatically. We apply our model to gender discourse communities (e.g., r/TheRedPill, r/MGTOW, r/FemaleDatingStrategy) to detect the level of toxicity toward genders (i.e., sexism). Our results show that our framework accurately and consistently (93% correlation) measures the level of sexism in a community. We finally discuss how our framework can be generalized in the future to measure qualities other than toxicity (e.g. sentiment, humor) toward general-purpose targets and turn into an indicator of different sorts of polarizations.